
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score, cross_validate
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score
from sklearn.metrics import plot_confusion_matrix
from sklearn.metrics import balanced_accuracy_score
from sklearn.metrics import confusion_matrix
from lightgbm import LGBMClassifier
from sklearn.model_selection import GridSearchCV

def lgb_model_init(x_train,y_train):
    lgb_clf=LGBMClassifier()
    lgb_clf.fit(x_train,y_train)
    
    return lgb_clf

def lgb_model_tuned(x_train,y_train):
    
    grid_search = {'num_leaves': [11,21,31],
               'max_depth': [5,8,10],
               'min_data_in_leaf': [10, 15, 20],
               'learning_rate': [0.1,0.003,0.001],
               'n_estimators': [50,100]}
    clf = LGBMClassifier()
    grid = GridSearchCV(estimator = clf, param_grid = grid_search, 
                               cv = 4, verbose= 5, n_jobs = -1)
    grid.fit(x_train,y_train)
    
    lgb_model=grid.best_estimator_
    return lgb_model


data = pd.read_csv('preprocessing1.csv', encoding = 'cp949', index_col=0 )
seed = 5764

target=data['Status']
data.drop(['Status'],axis=1,inplace = True)

# 학습, 테스트 데이터 분리 (0.7:0.3)
x_train, x_test, y_train, y_test = train_test_split(data, target, test_size=0.3, random_state=seed)


lgb_model = lgb_model_tuned(x_train,y_train)

Fitting 4 folds for each of 162 candidates, totalling 648 fits
[LightGBM] [Warning] min_data_in_leaf is set=10, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=10


import numpy as np
from lime import lime_tabular

lgb_lime_explainer = lime_tabular.LimeTabularExplainer(np.array(x_train),feature_names = data.columns , mode="classification")
for i in range(0,10):
    lgb_lime_explanation = lgb_lime_explainer.explain_instance(x_test.iloc[i], lgb_model.predict_proba)
    lgb_lime_explanation.show_in_notebook(show_table=True)
    print(i)


import shap 

lgb_shap_explainer = shap.TreeExplainer(lgb_model)
shap_values = lgb_shap_explainer.shap_values(x_test)

LightGBM binary classifier with TreeExplainer shap values output has changed to a list of ndarray


shap_values0 = shap_values[0]
shap_values1 = shap_values[1]


pd.DataFrame(shap_values)


shap.summary_plot(shap_values1, x_test)


shap.dependence_plot('rate_of_interest', shap_values1, x_test)


shap.dependence_plot('Upfront_charges', shap_values1, x_test)

	0	1	2	3	4	5	6	7	8	9	...	53	54	55	56	57	58	59	60	61	62
0	-0.060913	5.938066	0.033142	1.337879	-0.027559	-0.106786	0.021367	-0.001228	0.037628	-0.039497	...	0.018534	-0.067606	-0.011855	0.000306	0.0	0.0	0.0	0.000042	0.0	0.0
1	-0.100589	1.817809	0.293854	1.327279	-0.018856	-0.039895	-0.043687	-0.000038	-0.111681	-0.013843	...	-0.016512	0.000143	0.027499	-0.002004	0.0	0.0	0.0	-0.000036	0.0	0.0
2	0.031435	-0.244622	0.277961	-2.480536	-0.019976	-0.036943	-0.047571	-0.000558	-0.066861	-0.110250	...	-0.003020	0.000013	-0.062603	0.000276	0.0	0.0	0.0	-0.000036	0.0	0.0
3	-0.027382	-1.116766	0.089582	0.444481	-0.035768	-0.047052	0.044000	-0.000556	-0.043766	-0.185097	...	0.011086	0.000013	-0.003684	0.000306	0.0	0.0	0.0	-0.000023	0.0	0.0
4	-0.127619	-1.709845	-0.031520	0.342881	-0.082172	-0.113986	-0.047293	-0.000097	-0.096669	-0.116064	...	0.003405	0.000013	-0.005900	0.000394	0.0	0.0	0.0	-0.000070	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
43190	-0.068260	-0.434048	0.431488	0.749769	-0.040857	-0.056899	-0.025563	-0.000027	-0.096319	-0.131242	...	0.016858	-0.067606	0.002202	0.000111	0.0	0.0	0.0	0.000043	0.0	0.0
43191	-0.028327	-1.323152	-0.603167	0.558277	-0.094224	-0.143546	0.021240	-0.000075	0.405080	-0.057729	...	0.003336	-0.067606	-0.043265	0.000298	0.0	0.0	0.0	0.000042	0.0	0.0
43192	0.003222	-1.274914	-0.733685	0.548409	-0.085359	-0.148556	0.015578	0.000037	-0.011205	-0.073217	...	0.001393	-0.067606	-0.052286	0.000405	0.0	0.0	0.0	-0.000035	0.0	0.0
43193	-0.139284	-0.600632	-0.267923	-2.245942	-0.014664	-0.049018	-0.051033	-0.001116	-0.055401	-0.116513	...	-0.020422	-0.067606	0.079443	-0.001919	0.0	0.0	0.0	-0.000070	0.0	0.0
43194	-0.033462	-1.149843	0.330163	0.162047	-0.023018	-0.037735	-0.065641	0.000041	-0.042708	-0.099078	...	-0.006054	-0.067606	0.040033	-0.001517	0.0	0.0	0.0	-0.000036	0.0	0.0